Despite the capability to collect sophisticated multiparameter listmode data, both rectilinear or bit-map cell sorting boundaries still are usually chosen manually and in a rather arbitrary fashion. Usually the experimenter performs visual clustering prior to drawing boundaries which have no statistical prediction of successful classification. Visualization of complex multiparameter data is also difficult. One way to deal with the visualization problem is to view the first three principal components of the data and use this information to estimate the number and approximate centroids for "guided" cluster analysis. Cluster membership probabilities will then be used to make sort decisions. Another way to deal with the problem of placing sort boundaries on the basis of arbitrary "visual classifications" is to apply statistical methods of classifying cells, e.g. discriminant analysis with Bayes decision boundaries. Discriminant functions will be calculated and Bayes decision boundaries will be used to sort cells on the basis of discriminant function scores which will be calculated in real-time by hardware and/or software lookup tables. A cost of misclassification will also be included in the cell sorting decision. For all classifier systems developed, classifier performance will be measured through ROC ("receiver operating characteristics") analyses of true-positives and false-positives. To accomplish this we will use a well-defined system of data and model cell systems whereby all classifiers can be checked for correctness against "tagged" parameters. All sorted model cells can be unequivocally identified by PCR (polymerase chain reaction) or by FISH (fluorescence in-situ hybridization). While the main focus of the proposal is to develop real-time cell classifiers useful for cell sorting, many of the techniques can also be used by other researchers for off-line analysis of conventional listmode flow cytometry data. Hence many of these techniques should prove important to other researchers even if they are unable to perform the sophisticated cell sorting described in this proposal. To demonstrate the importance of these new techniques to many problems in biology and medicine we will attempt to apply these new techniques to several important applications including: (1) high-resolution sorting of single fetal cells from human maternal blood for prenatal diagnosis; (2) molecular characterizations of oncogene, tumor suppresser, metastatic, and multi-drug resistance genes in rare human metastatic breast cancer cells isolated from peripheral blood and bone marrow by high-speed enrichment or high-resolution cell sorting; and (3) bone marrow purging of metastatic cells to allow for autologous transplantations in breast cancer patients undergoing high-dose chemotherapy.